From 6faa813652c55dac15ae41457d30d90a9a0dcd22 Mon Sep 17 00:00:00 2001
From: NAUD Maxence <maxence.naud@cea.fr>
Date: Fri, 9 Feb 2024 09:53:07 +0000
Subject: [PATCH] [Add] small optimization
---
.../LeakyReLUImpl_forward_kernels.hpp | 2 +-
.../ReduceMeanImpl_forward_kernels.hpp | 96 +++++++++++--------
2 files changed, 58 insertions(+), 40 deletions(-)
diff --git a/include/aidge/backend/cpu/operator/LeakyReLUImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/LeakyReLUImpl_forward_kernels.hpp
index 761b9579..d10b32e1 100644
--- a/include/aidge/backend/cpu/operator/LeakyReLUImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/LeakyReLUImpl_forward_kernels.hpp
@@ -25,7 +25,7 @@ void LeakyReLUImpl_cpu_forward_kernel(const LeakyReLU_Op::Attrs& attrs,
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
- I negativeSlope = static_cast<I>(std::get<0>(attrs));
+ const I negativeSlope = static_cast<const I>(std::get<0>(attrs));
for (std::size_t i = 0; i < inputLenght; ++i) {
output[i] = input[i] >= 0 ? input[i] : input[i] * negativeSlope;
diff --git a/include/aidge/backend/cpu/operator/ReduceMeanImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/ReduceMeanImpl_forward_kernels.hpp
index 71888aa5..bc9ada0f 100644
--- a/include/aidge/backend/cpu/operator/ReduceMeanImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ReduceMeanImpl_forward_kernels.hpp
@@ -33,56 +33,74 @@ void ReduceMeanImpl_cpu_forward_kernel(const typename ReduceMean_Op<DIM>::Attrs&
O* output = static_cast<O*>(output_);
const std::size_t nb_dims = inputDims.size();
-
const std::size_t totalElements = std::accumulate(inputDims.cbegin(), inputDims.cend(), 1, std::multiplies<std::size_t>());
- std::size_t outputElements = totalElements;
- std::size_t *stride_post = new std::size_t[nb_dims];
- stride_post[nb_dims - 1] = 1;
- for (std::size_t i = nb_dims-2; i != static_cast<std::size_t>(-1); --i) {
- stride_post[i] = stride_post[i+1]*inputDims[i+1];
- }
- std::size_t *stride_pre = new std::size_t[nb_dims];
- stride_pre[0] = 1;
- for (std::size_t i = 1; i < nb_dims; ++i) {
- stride_pre[i] = stride_pre[i-1]*inputDims[i-1];
- }
+ if (DIM == 1) {
+ const std::size_t stride_pre = std::accumulate(inputDims.cbegin(), inputDims.cbegin() + std::get<0>(attrs)[0], 1, std::multiplies<std::size_t>());
+ const std::size_t stride_post = std::accumulate(inputDims.crbegin(), inputDims.crbegin() + nb_dims -1 - std::get<0>(attrs)[0], 1, std::multiplies<std::size_t>());
- const I* inputAccumulation = input;
- I* outputAccumulation = nullptr;
-
- for (const std::size_t& a : std::get<0>(attrs)) {
- outputElements /= inputDims[a];
- outputAccumulation = new I[outputElements];
- const std::size_t dim_i = inputDims[a];
- for (std::size_t pre = 0; pre < stride_pre[a]; ++pre) {
- for (std::size_t post = 0; post < stride_post[a]; ++post) {
- const std::size_t idx_i = pre * dim_i * stride_post[a] + post;
- const std::size_t idx_o = pre * stride_post[a] + post;
- outputAccumulation[idx_o] = inputAccumulation[idx_i];
+ const std::size_t dim_i = inputDims[std::get<0>(attrs)[0]];
+ for (std::size_t pre = 0; pre < stride_pre; ++pre) {
+ for (std::size_t post = 0; post < stride_post; ++post) {
+ const std::size_t idx_i = pre * dim_i * stride_post + post;
+ const std::size_t idx_o = pre * stride_post + post;
+ output[idx_o] = input[idx_i];
for (std::size_t i = 1; i < dim_i; ++i) {
- outputAccumulation[idx_o] += inputAccumulation[idx_i + i*stride_post[a]];
+ output[idx_o] += input[idx_i + i*stride_post];
}
+ output[idx_o] /= dim_i;
}
}
- std::for_each(stride_pre+a+1, stride_pre+nb_dims, [dim_i] (std::size_t& val) { val /= dim_i; });
- if (inputAccumulation != input) {
- delete[] inputAccumulation;
+ } else {
+ std::size_t outputElements = totalElements;
+
+ std::size_t *stride_post = new std::size_t[nb_dims];
+ stride_post[nb_dims - 1] = 1;
+ for (std::size_t i = nb_dims-2; i != static_cast<std::size_t>(-1); --i) {
+ stride_post[i] = stride_post[i+1]*inputDims[i+1];
+ }
+ std::size_t *stride_pre = new std::size_t[nb_dims];
+ stride_pre[0] = 1;
+ for (std::size_t i = 1; i < nb_dims; ++i) {
+ stride_pre[i] = stride_pre[i-1]*inputDims[i-1];
}
- inputAccumulation = outputAccumulation;
- }
- // Copy elements from inputAccumulation to output while dividing by divisor
- I divisor = totalElements / outputElements;
- std::transform(inputAccumulation, inputAccumulation + outputElements, output,
- [divisor](int element) { return element / divisor; });
- if (outputAccumulation) {
- delete[] outputAccumulation;
- }
- delete[] stride_post;
- delete[] stride_pre;
+ const I* inputAccumulation = input;
+ I* outputAccumulation = nullptr;
+ for (const std::size_t& a : std::get<0>(attrs)) {
+ outputElements /= inputDims[a];
+ outputAccumulation = new I[outputElements];
+ const std::size_t dim_i = inputDims[a];
+ for (std::size_t pre = 0; pre < stride_pre[a]; ++pre) {
+ for (std::size_t post = 0; post < stride_post[a]; ++post) {
+ const std::size_t idx_i = pre * dim_i * stride_post[a] + post;
+ const std::size_t idx_o = pre * stride_post[a] + post;
+ outputAccumulation[idx_o] = inputAccumulation[idx_i];
+ for (std::size_t i = 1; i < dim_i; ++i) {
+ outputAccumulation[idx_o] += inputAccumulation[idx_i + i*stride_post[a]];
+ }
+ }
+ }
+ std::for_each(stride_pre+a+1, stride_pre+nb_dims, [dim_i] (std::size_t& val) { val /= dim_i; });
+ if (inputAccumulation != input) {
+ delete[] inputAccumulation;
+ }
+ inputAccumulation = outputAccumulation;
+ }
+
+ // Copy elements from inputAccumulation to output while dividing by divisor
+ I divisor = totalElements / outputElements;
+ std::transform(inputAccumulation, inputAccumulation + outputElements, output,
+ [divisor](int element) { return element / divisor; });
+ if (outputAccumulation) {
+ delete[] outputAccumulation;
+ }
+ delete[] stride_post;
+ delete[] stride_pre;
+ }
}
+
namespace {
// DIM = 1
static Registrar<ReduceMeanImpl1DForward_cpu> registrarReduceMeanImplForward_1D_cpu_Float32(
--
GitLab